{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utilizing Renewables and Energy Storage to Minimize Electricity Use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Using renewable energy has gained popularity with the adoption of solar and wind energy production technologies. Yet, the sun is not always shining and the wind is not always blowing. When customers need energy might not coincide with those of high wind and solar potential which is a key challenge associated with renewable energy sources. Batteries can be a solution to this problem making it possible to use energy that has been stored can be used at any time during the day by discharging the energy stored in the battery.\n",
    "\n",
    "In this example, we will utilize solar energy along with battery storage to satisfy the energy demands of operating a university building and the associated courses that are taking place. In the next sections, we will describe the problem statement, and demonstrate how data science and mathematical optimization can be used to optimally solve the given problem.\n",
    "\n",
    "The information used for this example have been adopted from [IEEE's Predict+Optimize Technical Challenge](https://ieee-dataport.org/competitions/ieee-cis-technical-challenge-predictoptimize-renewable-energy-scheduling)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Objective and Prerequisites\n",
    "\n",
    "In this example, we consider a single university building on campus in Melbourne, Australia that has a set of courses scheduled over the course of six days (Monday - Saturday). \n",
    "\n",
    "The building and each course it holds have a certain energy demand that needs to be satisfied. The building's energy demand is assumed to known and each of the courses has a different demand based on the resources required (e.g. class size or a lab). The building has a solar panel installed which is capable of providing energy directly while there is also the ability of purchasing power directly from the electricity grid to satisfy the demand. Additionally, the building is equipped with two batteries which can store the energy provided from the solar panels, and can provide this energy when needed. We also allow for the batteries to be charged from the grid. \n",
    "\n",
    "This example will integrate time series data forecasting with mathematical optimization as we will incorporate a solar generation forecast as the key input for an optimization problem. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Problem Statement\n",
    "\n",
    "Given a course schedule for the upcoming week and historical data about the solar potential, the objective is to determine the optimal battery charging and discharging schedule are in order to satisfy the energy demand for a building where the courses are held. The total energy demand is composed of the demand from the building itself plus the amount needed for each course. The example will look at two objectives:\n",
    "- Minimize the total electricity purchased from the grid during the upcoming week\n",
    "- Since electricity prices fluctuate, minimize the total cost of electricity purchased from the grid\n",
    "\n",
    "At the same time, batteries have a finite capacity as well as limits on how much it can charge or discharge over a period of time. These impose constraints on the problem and make it difficult to solve.\n",
    "\n",
    "For simplicity, the time considered for each day will be limited to between 5am and 8pm. If you have access to a full Gurobi license (e.g. through our [Academic Program](https://www.gurobi.com/academia/academic-program-and-licenses/)) feel free to expand this to solve a much larger version of the problem. \n",
    "\n",
    "Here is a view of what the week's schedule will look like:\n",
    "![sched](ClassSchedule.png)\n",
    "\n",
    "## Solution Approach\n",
    "\n",
    "The solution approach of the problem consists of two components: 1) **a forecasting component** for the solar availability and 2) **an optimization component** to determine the battery schedule as well as the amount of electricity purchased from the grid.\n",
    "\n",
    "### Solar Power Forecasting\n",
    "\n",
    "The forecasting component was completed in the [energy_storage_ML](energy_storage_ML.ipynb) notebook in this folder. We'll use a given forecast from that model, as well as the demand of building and each of the courses to formulate and solve a mixed-integer programming (MIP) problem to find the optimal solution to the problem for each of the above objectives using gurobipy.\n",
    "\n",
    "We begin our solution approach by installing and loading the necessary packages which will be needed:\n",
    "\n",
    "### Optimal Battery Schedule\n",
    "Now that we have a forecast of solar generation, we will create an optimization model that will schedule the charging and discharging of two batteries to help meet the building and course energy demand.\n",
    "\n",
    "There are two batteries available that can be charged and discharged. Hence, we need an index to describe the existence and operation of each of the batteries. Finally, given that we would like to create the battery schedule in 30-minute intervals for the next month, we need to define an index for the time periods too. \n",
    "\n",
    "**Sets**\n",
    "\n",
    "$B = \\{\\texttt{Battery0, Battery1}\\}$\n",
    "\n",
    "$T = \\{0,1,...,179\\}$ for the first Monday through Saturday of October, 2020\n",
    "\n",
    "**Indices**\n",
    "\n",
    "$b$: denotes the battery (0 or 1)\n",
    "\n",
    "$t$: denotes the time periods (from 0 to 179) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install gurobipy\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import gurobipy as gp\n",
    "from gurobipy import GRB\n",
    "\n",
    "batteries = [\"Battery0\", \"Battery1\"]\n",
    "path = 'https://raw.githubusercontent.com/Gurobi/modeling-examples/master/optimization101/Modeling_Session_2/'\n",
    "solar_values_read = pd.read_csv(path+'pred_solar_values.csv')\n",
    "# solar_values_read = pd.read_csv('pred_solar_values.csv')\n",
    "time_periods = range(len(solar_values_read))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input Parameters\n",
    "\n",
    "Each of the batteries has a fixed capacity of how much energy it can store, as well as a maximum amount of energy that can be stored or discharged for each 30-minute period.\n",
    "\n",
    "$c_{b}$: capacity of battery $b \\in B \\quad\\quad \\texttt{capacity[b]}$\n",
    "\n",
    "$p_{b}$: loss of energy (as a percentage) during transfer into battery $b\\in B \\quad\\quad \\texttt{p}\\_\\texttt{loss[b]}$\n",
    "\n",
    "$q_{b}$: quantity of initial energy in battery $b \\in B \\quad\\quad \\texttt{initial[b]}$\n",
    "\n",
    "$solar_{t}$: solar power generation of the panel for time period $t \\in T \\quad\\quad \\texttt{solar}\\_\\texttt{values[b]}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "capacity = {\"Battery0\": 60, \"Battery1\": 80} # in Kw\n",
    "p_loss = {\"Battery0\": 0.95, \"Battery1\": 0.9} # proportion\n",
    "initial = {\"Battery0\": 0, \"Battery1\": 0} # in kW\n",
    "\n",
    "solar_values = round(solar_values_read.yhat,3)\n",
    "solar_values.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The energy demand at a particular time period comes from two sources -- the building and the class -- and we are only concerned with the total demand. This data will be read in from external files and are already ordered by time period correctly. \n",
    "\n",
    "$d_{t}$: total building and class energy demand for time period $t\\in T \\quad\\quad \\texttt{total}\\_\\texttt{deamnd[t]}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Solar Generation: 4939.172 \n",
      "Total Demand: 5250.6\n"
     ]
    }
   ],
   "source": [
    "schedule = pd.read_csv(path+'schedule_demand.csv')\n",
    "# schedule = pd.read_csv('schedule_demand.csv')\n",
    "avg_building = pd.read_csv(path+'building_demand.csv')\n",
    "# avg_building = pd.read_csv('building_demand.csv')\n",
    "total_demand = schedule.sched_demand + avg_building.build_demand\n",
    "print(f\"Total Solar Generation: {solar_values.sum()} \\nTotal Demand: {total_demand.sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Variables\n",
    "\n",
    "The decisions for this problem are \n",
    "- whether each battery is charged or discharged at a given time period\n",
    "- how much to charge or discharge each battery\n",
    "- the current state of the battery at a given time period\n",
    "- as well as how much energy is purchased from the grid\n",
    "\n",
    "Let $f^{in}_{b,t}$ be the amount each battery, $b$, can `charges` at time period,  $t$, $\\forall b\\in B, t\\in T$. $\\quad\\quad \\texttt{flow}\\_\\texttt{in[b,t]}$\n",
    "\n",
    "Let $f^{out}_{b,t}$ be the amount each battery `discharges`, similarly. $\\quad\\quad \\texttt{flow}\\_\\texttt{out[b,t]}$\n",
    "\n",
    "Set the max amount that each battery can charge or discharge in a single period to be 20 kW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = gp.Model() #this defines the model that we'll add to as we finish the formulation\n",
    "\n",
    "flow_in = m.addVars(batteries, time_periods, name=\"flow_in\") \n",
    "flow_out = m.addVars(batteries, time_periods, name=\"flow_out\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, there is the amount of energy that is purchased from the grid for each time period. We will assume this is a non-negative number and that we cannot \"sell back\" to the grid (although this is an interesting problem as well!)\n",
    "\n",
    "$grid_{t}$ : This variable indicates the amount of energy purchased from the grid at time period, $t$, $\\forall t \\in T$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = m.addVars(time_periods, name=\"grid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next two sets of decision variables will have a slightly different feel to them. We need to keep track of the amount of energy that is in each battery (i.e. its state) over each time period and we need to track of how much solar energy is used (by charging or directly supplying energy). \n",
    "\n",
    "$s_{b,t}$ is the current amount of energy in battery, $b$, at the end of time period, $t, \\forall b\\in B, t\\in T$. $\\quad\\texttt{state[b,t]}$\n",
    "\n",
    "$gen_{t}$ is the amount of available solar energy that is used in period $t$, $\\forall t \\in T$. $\\quad\\texttt{gen[t]}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "state = m.addVars(batteries, time_periods, name=\"state\") \n",
    "gen = m.addVars(time_periods, name=\"gen\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last decision variables to add will take care of a tricky instance that can arise in a problem like this -- a battery cannot simultaneously charge and discharge. Thus, we need to make sure each battery, at each time period, will be doing one of three things: 1. **charge**, 2. **discharge**, or 3. **neither**. \n",
    "\n",
    "To model this we introduce a binary variable for each time period, $z_{b,t}, \\forall b \\in B, t \\in T$, which we'll call $\\texttt{zwitch}\\_\\texttt{[b,t]}$. Why *zwitch*? It's something I've done since my early optimization modeling days since a common single letter used for binary variables used like this in MO is $z$. This variable acts like switch -- hence, *zwitch*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "zwitch = m.addVars(batteries, time_periods, vtype=GRB.BINARY, name=\"zwitch\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constraints\n",
    "In each period, we need to make sure we meet energy demand. The available energy can come from three sources: 1) the grid, 2) solar panel, or 3) battery discharge. Additionally, we may decide to charge a battery which will help meet demand in a later period. \n",
    "\n",
    "$$\n",
    "\\begin{align*} \n",
    "\\sum_b(f^{out}_{b,t}-p_bf^{in}_{b,t}) + gen_t + grid_t = d_t \\quad \\forall t \\in T\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.addConstrs((gp.quicksum(flow_out[b,t] - p_loss[b]*flow_in[b,t] for b in batteries) + gen[t] + grid[t] == total_demand[t] \n",
    "                  for t in time_periods), name=\"power_balance\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The state of each battery at a given time period affects the following state, along with the decision to charge or discharge each battery. So in general a battery's state at time $t$ is the state in the prior period and any charge or discharge. \n",
    "\n",
    "To do this, we set an initial state based on any initial energy each battery has, along with the first time period's charge/discharge decision. \n",
    "\n",
    "\\begin{equation}\n",
    "s_{b,0} = q_b + p_bf^{in}_{b,0} - f^{out}_{b,0}\n",
    "\\end{equation}\n",
    "\n",
    "For each time period after (i.e. $t\\ge1$), the state of a battery is found by:\n",
    "\n",
    "\\begin{equation}\n",
    "s_{b,t} = s_{b,t-1} + p_bf^{in}_{b,t} - f^{out}_{b,t}, t \\ge 1\n",
    "\\end{equation}\n",
    "\n",
    "The respective constraints can be written as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.addConstrs((state[b,0] == initial[b] + p_loss[b]*flow_in[b,0] - flow_out[b,0] for b in batteries), name=\"initial_state\")\n",
    "m.addConstrs((state[b,t] == state[b,t-1] + p_loss[b]*flow_in[b,t] - flow_out[b,t] for b in batteries for t in time_periods if t >= 1), name=\"subsequent_states\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The availability of solar energy can be used in three places:\n",
    "1. Battery0\n",
    "2. Battery1\n",
    "3. Directly to satisfy demand\n",
    "\n",
    "Because of this we need to limit the amount the batteries can charge along with the amount used directly for demand. \n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "f^{in}_{\\texttt{Battery0},t} + f^{in}_{\\texttt{Battery1},t} + gen_t \\le solar_t, \\quad \\forall t \\in T\n",
    "\\end{equation}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.addConstrs((flow_in['Battery0',t] + flow_in['Battery1',t] + gen[t] <= solar_values[t] for t in time_periods), name = \"solar_avail\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary Variables Modeling Logic\n",
    "Using binary variables to model alternatives like what we have here is arguably the most difficult part of mathematical optimization modeling. It comes down to be able to represent a complex system using zeros, ones, and inequalities. To get the logic correct takes time and there is a lot of iteration at point to make sure it's done correctly. In short, do not worry if you find this part difficult. \n",
    "\n",
    "Let's formulate the constraints and then dive into why they work. \n",
    "\n",
    "$$\n",
    "\\begin{align*} \n",
    "f^{in}_{b,t} &\\leq 20*z_{b,t} &\\forall b \\in B, t \\in T \\\\\n",
    "f^{out}_{b,t} &\\leq 20*(1-z_{b,t}) &\\forall b \\in B, t \\in T\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "To illustrate how this works let's consider an example -- suppose for one of batteries $f^{in}_{b,t} = 10$ in a time period. For the first inequality to hold it is *necessary* that $z_{b,t} = 1$, otherwise it is $0$ and $20*0 = 0$, which can't happen if $f^{in}_{b,t} = 10$. Then for the same time period, if $z_{b,t} = 1$, then  $1-z_{b,t} = 0$. This forces the right-hand side of the second inequality to be $0$ and this forces $f^{out}_{b,t} = 0$ making it impossible to discharge during this period. Similar logic applies if the battery *discharges* during a period (i.e. suppose $f^{out}_{b,t} = 10$).\n",
    "\n",
    "With our `zwitch` being a binary variable, that means one of the two above cases will *always* happen for each time period. The important question -- is this a problem? The answer is no, and that's because we need to worry about what our inequalities *force* decision variables to be. Consider the case when $f^{in}_{b,t} = f^{in}_{b,t} = 0$, meaning a battery is neither charging nor discharging since the flow in and out is zero. This case won't violate either of the inequalities above and doesn't matter which value $z$ takes -- it can be $0$ or $1$ and the inequalities hold. \n",
    "\n",
    "This does raise an important point. If after solving this problem you were asked \"How many periods did the battery charge and discharge?\" It's easy to think that $z_{b,t} = 1$ means the battery is charging, so just add those up to get the number of charging periods. Then count the number of times if $z_{b,t} = 0$ to get the discharge count. This would be *incorrect* given what we saw when $f^{in}_{b,t} = f^{in}_{b,t} = 0$. \n",
    "\n",
    "Where did the $20$ come from? It's the upper bound on the amount a battery can charge or discharge during a period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.addConstrs((flow_in[b,t] <= 20*zwitch[b,t] for b in batteries for t in time_periods), name = \"to_charge\")\n",
    "m.addConstrs((flow_out[b,t] <= 20*(1-zwitch[b,t]) for b in batteries for t in time_periods), name = \"or_not_to_charge\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we need to set the max amount each battery can store, which means the battery's *state* must be bounded. These upper bounds can be set in the variable definition when using `addVars()`. Doing so there would require a list with the same dimensions as the decision variable. Below is another way to set the bound that quickly uses a `for` loop. For more info on all of the parameters you can set when adding variables see the [documentation]('https://www.gurobi.com/documentation/10.0/refman/py_model_addvars.html'). Generally, it's more efficient to set the bounds when adding the variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for b, t in state:\n",
    "    state[b,t].UB = capacity[b]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{align*} \n",
    "s_{b,t} \\le c_b, \\quad \\forall b \\in B\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Objective Function\n",
    "\n",
    "The objective is to minimize the total amount of energy purchased from the grid over all time periods.\n",
    "\n",
    "\\begin{equation}\n",
    "{\\rm minimize} \\quad \\sum_{t} g_{t}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.setObjective(gp.quicksum(grid[t] for t in time_periods), GRB.MINIMIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solving the Optimization Model and Output Analysis\n",
    "With the model now set, we can optimize for minimal electricity purchased. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)\n",
      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 1440 rows, 1800 columns and 4498 nonzeros\n",
      "Model fingerprint: 0xaa31bb6a\n",
      "Variable types: 1440 continuous, 360 integer (360 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [9e-01, 2e+01]\n",
      "  Objective range  [1e+00, 1e+00]\n",
      "  Bounds range     [1e+00, 8e+01]\n",
      "  RHS range        [1e-01, 9e+01]\n",
      "Found heuristic solution: objective 5250.6000000\n",
      "Presolve removed 84 rows and 264 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 1356 rows, 1536 columns, 4110 nonzeros\n",
      "Found heuristic solution: objective 4995.4820000\n",
      "Variable types: 1200 continuous, 336 integer (336 binary)\n",
      "\n",
      "Root relaxation: objective 1.281678e+03, 616 iterations, 0.01 seconds (0.02 work units)\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0 1281.67768    0    1 4995.48200 1281.67768  74.3%     -    0s\n",
      "H    0     0                    1281.6776848 1281.67768  0.00%     -    0s\n",
      "     0     0 1281.67768    0    1 1281.67768 1281.67768  0.00%     -    0s\n",
      "\n",
      "Explored 1 nodes (616 simplex iterations) in 0.04 seconds (0.03 work units)\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 3: 1281.68 4995.48 5250.6 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 1.281677684779e+03, best bound 1.281677684779e+03, gap 0.0000%\n",
      "Total energy purchased from the grid: 1281.678 kWh\n"
     ]
    }
   ],
   "source": [
    "m.optimize()\n",
    "print(f\"Total energy purchased from the grid: {round(m.objVal,3)} kWh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now take a look at the values of our some of the decision variables. The *getAttr* function along with the *'X'* argument is used to get the values of the `state` decision variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Periods Battery0 charges: 52\n",
      "Periods Battery1 charges: 26\n",
      "Periods Battery0 discharges: 44\n",
      "Periods Battery1 discharges: 23\n"
     ]
    }
   ],
   "source": [
    "sol_in = pd.Series(m.getAttr('X',flow_in))\n",
    "sol_out = pd.Series(m.getAttr('X',flow_out))\n",
    "sol_level = pd.Series(m.getAttr('X',state))\n",
    "\n",
    "print(f\"Periods Battery0 charges: {sum(sol_in['Battery0'] > 0)}\")\n",
    "print(f\"Periods Battery1 charges: {sum(sol_in['Battery1'] > 0)}\")\n",
    "print(f\"Periods Battery0 discharges: {sum(sol_out['Battery0'] > 0)}\")\n",
    "print(f\"Periods Battery1 discharges: {sum(sol_out['Battery1'] > 0)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A plot of each battery's state over the time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Periods at Battery0 at Full Capacity: 24\n",
      "Periods at Battery1 at Full Capacity: 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "s0, = plt.plot(sol_level['Battery0'], c = 'orange')\n",
    "s1, = plt.plot(sol_level['Battery1'], c = 'blue')\n",
    "plt.ylabel('Battery State (kWhr)')\n",
    "plt.xlabel('Time Period')\n",
    "plt.legend([s0,s1],[\"Battery0\", \"Battery1\"])\n",
    "plt.axhline(y=capacity['Battery0'], c='orange', linestyle='--', alpha = 0.5)\n",
    "plt.axhline(y=capacity['Battery1'], c='blue', linestyle='--', alpha = 0.5)\n",
    "print(f\"Periods at Battery0 at Full Capacity: {sum(sol_level['Battery0']==capacity['Battery0'])}\")\n",
    "print(f\"Periods at Battery1 at Full Capacity: {sum(sol_level['Battery1']==capacity['Battery1'])}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the default capacity values, the smaller capacity battery (Battery0) hits its capacity only twice while Battery1 hits its limit more often. How would changes to these values change the optimization?\n",
    "\n",
    "Our last set of decision variables is the amount of electricity to buy from the grid, in which the total was minimized in the objective, and plot that over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of time periods where electrity is purchaced from the grid: 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sol_grid = pd.Series(m.getAttr('X',grid))\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(sol_grid)\n",
    "plt.ylabel('Power Purchased (kWhr)')\n",
    "plt.xlabel('Time Period');\n",
    "print(f\"Proportion of time periods where electrity is purchaced from the grid: {round(sum(sol_grid > 0)/len(sol_grid),3)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, the number of periods where electricity was purchased from the grid was calculated. Is this the same as minimizing the total amount purchased? If not (hint, it's not!), how would you model that? (Another hint, that would require additional binary variables)\n",
    "### Changing the Objective Function\n",
    "As mentioned at the beginning of the example, we'll consider two objective functions with the second minimizing the cost of electricity purchaced. To do that, we need anticipated prices for each time period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_price = pd.read_csv(path+'expected_price.csv')\n",
    "# avg_price = pd.read_csv('expected_price.csv')\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(avg_price.index, avg_price.price, '-o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change the objective, it just needs to be set again. The rest of the model stays the same so no further adjustments are needed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.setObjective(gp.quicksum(avg_price.price[time]*grid[time] for time in time_periods), GRB.MINIMIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)\n",
      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 1440 rows, 1800 columns and 4498 nonzeros\n",
      "Model fingerprint: 0x66bddde2\n",
      "Variable types: 1440 continuous, 360 integer (360 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [9e-01, 2e+01]\n",
      "  Objective range  [6e-02, 2e+00]\n",
      "  Bounds range     [1e+00, 8e+01]\n",
      "  RHS range        [1e-01, 9e+01]\n",
      "\n",
      "MIP start from previous solve produced solution with objective 606.21 (0.02s)\n",
      "Loaded MIP start from previous solve with objective 606.21\n",
      "\n",
      "Presolve removed 84 rows and 264 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 1356 rows, 1536 columns, 4110 nonzeros\n",
      "Variable types: 1200 continuous, 336 integer (336 binary)\n",
      "\n",
      "Root relaxation: objective 6.019761e+02, 757 iterations, 0.01 seconds (0.02 work units)\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  601.97612    0    1  606.21048  601.97612  0.70%     -    0s\n",
      "H    0     0                     601.9761241  601.97612  0.00%     -    0s\n",
      "     0     0  601.97612    0    1  601.97612  601.97612  0.00%     -    0s\n",
      "\n",
      "Explored 1 nodes (757 simplex iterations) in 0.05 seconds (0.03 work units)\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 2: 601.976 606.21 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 6.019761240991e+02, best bound 6.019761240990e+02, gap 0.0000%\n",
      "Energy cost for the week: $601.98\n"
     ]
    }
   ],
   "source": [
    "m.optimize() # run the optimization again\n",
    "print(f\"Energy cost for the week: ${round(m.objVal,2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can extract the decision variables as before to see how the solution changes given the new objective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Periods at Battery0 at Full Capacity: 27\n",
      "Periods at Battery1 at Full Capacity: 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sol_level_cost = pd.Series(m.getAttr('X',state))\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(sol_level_cost['Battery0'], c ='orange')\n",
    "plt.plot(sol_level_cost['Battery1'], c ='blue')\n",
    "plt.legend([s0,s1],[\"Battery0\", \"Battery1\"])\n",
    "plt.axhline(y=capacity['Battery0'], c='orange', linestyle='--', alpha = 0.5)\n",
    "plt.axhline(y=capacity['Battery1'], c='blue', linestyle='--', alpha = 0.5)\n",
    "print(f\"Periods at Battery0 at Full Capacity: {sum(sol_level_cost['Battery0']==capacity['Battery0'])}\")\n",
    "print(f\"Periods at Battery1 at Full Capacity: {sum(sol_level_cost['Battery1']==capacity['Battery1'])}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that each battery spends more time full or near-full, particuarly Battery0. This shows how adding cost will make batteries hold on to electricity to then be discharged during time periods where the cost is highest. \n",
    "\n",
    "Here are some follow-on questions to take a deeper dive into this problem:\n",
    "- How much electricity was purchased from the grid after changing the objective?\n",
    "- Given the prices used for the seconds objective, what would the cost have been for the first solution that minimized electricity purchased?\n",
    "- How would increasing the capacity of Battery0 in the second objective change the solution?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<h2>Conclusion</h2>\n",
    "\n",
    "This example walked through an instance of how to model an energy system that leverages a time series forecast of solar availability. Part of the challenge in modeling such systems is how decisions from one time periods impact the next. We also looked at two ways to define a decision variables bounds and saw that updating an objective function is very straightforward, possibly having a big impact on the solution. There are more [energy related](https://www.gurobi.com/resource/electrical-power-generation-jupyter-notebook-i-and-ii/) examples in Gurobi's extensive set of [notebook examples](https://www.gurobi.com/resource/modeling-examples-using-the-gurobi-python-api-in-jupyter-notebook/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2022 Gurobi Optimization, LLC"
   ]
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